redis

Caching with Redis stores frequently accessed data in memory, eliminating repeated database queries. Implement cache-aside patterns to check Redis first, fetch from the database on miss, and store results with TTL expiration to balance freshness and performance.

Yes. Redis data structures enable session storage with automatic expiration and rate limiting by tracking request counts per key. Both patterns leverage Redis's fast in-memory operations and atomic commands for reliable, millisecond-latency coordination.

Use Redis pub/sub to publish messages to channels and subscribe listeners in real time. Publishers send events immediately; subscribers receive them asynchronously, enabling event-driven architectures without message durability requirements.

Distributed locks in Redis coordinate access across services using atomic operations and Lua scripts to prevent race conditions. Set expiring lock keys with unique identifiers; scripts verify ownership before releasing, guaranteeing exclusive access.

Connection pooling reuses TCP connections to reduce overhead; pipelining batches multiple commands into one request. Both are essential in production to handle concurrent load, lower latency, and maximize throughput with redis-py clients.